void topicCallback_input(const sensor_msgs::PointCloud2::ConstPtr& msg)
{
/* protected region user implementation of subscribe callback for input on begin */
pcl::PCLPointCloud2::Ptr pcl_pc(new pcl::PCLPointCloud2 ());
pcl::PointCloud<pcl::PointXYZ>::Ptr pcl_cloud (new pcl::PointCloud<pcl::PointXYZ>);
pcl::PCLPointCloud2::Ptr cloud_out (new pcl::PCLPointCloud2 ());
// Transformation into PCL type PointCloud2
pcl_conversions::toPCL((*msg), *(pcl_pc));
// Transformation into PCL type PointCloud<pcl::PointXYZRGB>
pcl::fromPCLPointCloud2(*(pcl_pc), *(pcl_cloud));
////////////////////////
// PassThrough filter //
////////////////////////
pcl::ConditionOr<pcl::PointXYZ>::Ptr range_cond (new pcl::ConditionOr<pcl::PointXYZ> ());
range_cond->addComparison (pcl::FieldComparison<pcl::PointXYZ>::ConstPtr (new
pcl::FieldComparison<pcl::PointXYZ> ("x", pcl::ComparisonOps::GT, robot_pose_x+localconfig.inhib_size)));
range_cond->addComparison (pcl::FieldComparison<pcl::PointXYZ>::ConstPtr (new
pcl::FieldComparison<pcl::PointXYZ> ("x", pcl::ComparisonOps::LT, robot_pose_x-localconfig.inhib_size)));
range_cond->addComparison (pcl::FieldComparison<pcl::PointXYZ>::ConstPtr (new
pcl::FieldComparison<pcl::PointXYZ> ("y", pcl::ComparisonOps::GT, robot_pose_y+localconfig.inhib_size)));
range_cond->addComparison (pcl::FieldComparison<pcl::PointXYZ>::ConstPtr (new
pcl::FieldComparison<pcl::PointXYZ> ("y", pcl::ComparisonOps::LT, robot_pose_y-localconfig.inhib_size)));
// build the filter
pcl::ConditionalRemoval<pcl::PointXYZ> condrem (range_cond);
condrem.setInputCloud (pcl_cloud);
condrem.setKeepOrganized(true);
// apply filter
condrem.filter (*pcl_cloud);
// Transformation into ROS type
pcl::toPCLPointCloud2(*(pcl_cloud), *(cloud_out));
pcl_conversions::moveFromPCL(*(cloud_out), output_pcloud2);
output_ready = true;
/* protected region user implementation of subscribe callback for input end */
}
void My_Filter::pclCallback(const sensor_msgs::PointCloud2::ConstPtr& cloud){
pcl::PCLPointCloud2::Ptr pcl_pc(new pcl::PCLPointCloud2 ());
pcl::PointCloud<pcl::PointXYZ>::Ptr pcl_cloud (new pcl::PointCloud<pcl::PointXYZ>);
pcl::PointCloud<pcl::PointXYZ>::Ptr final (new pcl::PointCloud<pcl::PointXYZ>);
pcl::PointCloud<pcl::PointXYZ>::Ptr final2 (new pcl::PointCloud<pcl::PointXYZ>);
pcl::PCLPointCloud2::Ptr cloud_out (new pcl::PCLPointCloud2 ());
sensor_msgs::PointCloud2 pc2;
std::vector<int> inliers;
// Transformation into PCL type PointCloud2
pcl_conversions::toPCL((*cloud), *(pcl_pc));
// Transformation into PCL type PointCloud<pcl::PointXYZRGB>
pcl::fromPCLPointCloud2(*(pcl_pc), *(pcl_cloud));
std::list<geometry_msgs::PoseStamped> tmp_list;
int robot_counter = 0;
for (std::list<geometry_msgs::PoseStamped>::iterator it = robots.begin() ; it != robots.end(); ++it)
{
if(it->pose.orientation.x > 0.001)
{ // Robot currently not seen
////////////////////////
// PassThrough filter //
////////////////////////
pcl::PassThrough<pcl::PointXYZ> pass;
pass.setInputCloud (pcl_cloud);
pass.setFilterFieldName ("x");
pass.setFilterLimits (it->pose.position.x-DETECTION_DISTANCE-it->pose.orientation.x*0.002,
it->pose.position.x+DETECTION_DISTANCE+it->pose.orientation.x*0.002);
//pass.setFilterLimitsNegative (true);
pass.filter (*final2);
pass.setInputCloud (final2);
pass.setFilterFieldName ("y");
pass.setFilterLimits (it->pose.position.y-DETECTION_DISTANCE-it->pose.orientation.x*0.002,
it->pose.position.y+DETECTION_DISTANCE+it->pose.orientation.x*0.002);
//pass.setFilterLimitsNegative (true);
pass.filter (*final2);
}
else
{ // Robot seen
////////////////////////
// PassThrough filter //
////////////////////////
pcl::PassThrough<pcl::PointXYZ> pass;
pass.setInputCloud (pcl_cloud);
pass.setFilterFieldName ("x");
pass.setFilterLimits (it->pose.position.x-DETECTION_DISTANCE, it->pose.position.x+DETECTION_DISTANCE);
//pass.setFilterLimitsNegative (true);
pass.filter (*final2);
pass.setInputCloud (final2);
pass.setFilterFieldName ("y");
pass.setFilterLimits (it->pose.position.y-DETECTION_DISTANCE, it->pose.position.y+DETECTION_DISTANCE);
//pass.setFilterLimitsNegative (true);
pass.filter (*final2);
pcl::ConditionOr<pcl::PointXYZ>::Ptr range_cond (new pcl::ConditionOr<pcl::PointXYZ> ());
range_cond->addComparison (pcl::FieldComparison<pcl::PointXYZ>::ConstPtr (new
pcl::FieldComparison<pcl::PointXYZ> ("x", pcl::ComparisonOps::GT, it->pose.position.x+DETECTION_DISTANCE)));
range_cond->addComparison (pcl::FieldComparison<pcl::PointXYZ>::ConstPtr (new
pcl::FieldComparison<pcl::PointXYZ> ("x", pcl::ComparisonOps::LT, it->pose.position.x-DETECTION_DISTANCE)));
range_cond->addComparison (pcl::FieldComparison<pcl::PointXYZ>::ConstPtr (new
pcl::FieldComparison<pcl::PointXYZ> ("y", pcl::ComparisonOps::GT, it->pose.position.y+DETECTION_DISTANCE)));
range_cond->addComparison (pcl::FieldComparison<pcl::PointXYZ>::ConstPtr (new
pcl::FieldComparison<pcl::PointXYZ> ("y", pcl::ComparisonOps::LT, it->pose.position.y-DETECTION_DISTANCE)));
// build the filter
pcl::ConditionalRemoval<pcl::PointXYZ> condrem (range_cond);
condrem.setInputCloud (pcl_cloud);
condrem.setKeepOrganized(true);
// apply filter
condrem.filter (*pcl_cloud);
}
if(final2->size() > 2)
{ // Something in the box
double meanX = 0.0;
double meanY = 0.0;
for(int i = 0; i < final2->size(); i++)
{
meanX += final2->at(i).x;
meanY += final2->at(i).y;
}
meanX = meanX / final2->size();
meanY = meanY / final2->size();
//robots.at(robot_counter).pose.position.x = meanX;
//robots.at(robot_counter).pose.position.y = meanY;
it->pose.position.x = meanX;
it->pose.position.y = meanY;
char numstr[50];
sprintf(numstr, "/robot_%s_link", it->header.frame_id.c_str());
t = tf::StampedTransform(tf::Transform(tf::createQuaternionFromYaw(0.0), tf::Vector3(meanX, meanY, 0.0)),
ros::Time::now(), "/world", numstr);
t.stamp_ = ros::Time::now();
broadcaster.sendTransform(t);
it->pose.orientation.x = 0.0;
tmp_list.push_front(*it);
//std::cout << "size : " << final2->size() << std::endl;
}
else
{ // Nothing found in the box
it->pose.orientation.x += 1.0;
if(it->pose.orientation.x > 100.0)
{
it->pose.orientation.x = 100.0;
}
//std::cout << "coef : " << it->pose.orientation.x << std::endl;
geometry_msgs::PoseStamped tmp = *it;
//robots.push_back(*it);
//robots.erase(it);
//robots.push_back(tmp);
tmp_list.push_back(*it);
}
robot_counter++;
}
robots = tmp_list;
/////////////////////////////////
// Statistical Outlier Removal //
/////////////////////////////////
/* pcl::StatisticalOutlierRemoval<pcl::PointXYZ> sor;
sor.setInputCloud (pcl_cloud);
sor.setMeanK (200);
sor.setStddevMulThresh (1.0);
sor.filter (*final);
*/
//////////////////////////////////
// Euclidian Cluster Extraction //
//////////////////////////////////
// Creating the KdTree object for the search method of the extraction
/* pcl::search::KdTree<pcl::PointXYZ>::Ptr tree (new pcl::search::KdTree<pcl::PointXYZ>);
tree->setInputCloud (pcl_cloud);
std::vector<pcl::PointIndices> cluster_indices;
pcl::EuclideanClusterExtraction<pcl::PointXYZ> ec;
ec.setClusterTolerance (0.05); // 2cm
ec.setMinClusterSize (5);
ec.setMaxClusterSize (50);
ec.setSearchMethod (tree);
ec.setInputCloud (pcl_cloud);
ec.extract (cluster_indices);
std::cout << cluster_indices.size() << std::endl;
double x = 0.0;
double y = 0.0;
for (std::vector<pcl::PointIndices>::const_iterator it = cluster_indices.begin (); it != cluster_indices.end (); ++it)
{
x = 0.0;
y = 0.0;
for (std::vector<int>::const_iterator pit = it->indices.begin (); pit != it->indices.end (); pit++)
{
x +=pcl_cloud->points[*pit].x;
y +=pcl_cloud->points[*pit].y;
}
x = x / it->indices.size();
y = y / it->indices.size();
std::cout << "x : " << x << " y : " << y << " size : " << it->indices.size() << std::endl;
}
*/
// Transformation into ROS type
pcl::toPCLPointCloud2(*(pcl_cloud), *(cloud_out));
pcl_conversions::moveFromPCL(*(cloud_out), pc2);
point_cloud_publisher_.publish(pc2);
}
void topicCallback_input_cloud(const sensor_msgs::PointCloud2::ConstPtr& msg)
{
/* protected region user implementation of subscribe callback for input_cloud on begin */
pcl::PCLPointCloud2::Ptr pcl_pc(new pcl::PCLPointCloud2 ());
pcl::PointCloud<pcl::PointXYZ>::Ptr pcl_cloud (new pcl::PointCloud<pcl::PointXYZ>);
pcl::PointCloud<pcl::PointXYZ>::Ptr final (new pcl::PointCloud<pcl::PointXYZ>);
pcl::PointCloud<pcl::PointXYZ>::Ptr final2 (new pcl::PointCloud<pcl::PointXYZ>);
pcl::PCLPointCloud2::Ptr cloud_out (new pcl::PCLPointCloud2 ());
sensor_msgs::PointCloud2 pc2;
std::vector<int> inliers;
// Transformation into PCL type PointCloud2
pcl_conversions::toPCL((*msg), *(pcl_pc));
// Transformation into PCL type PointCloud<pcl::PointXYZRGB>
pcl::fromPCLPointCloud2(*(pcl_pc), *(pcl_cloud));
std::list<geometry_msgs::PoseStamped> tmp_list;
int robot_counter = 0;
for (std::list<geometry_msgs::PoseStamped>::iterator it = robots.begin() ; it != robots.end(); ++it)
{
if(it->pose.orientation.x > 0.001)
{ // Robot currently not seen
////////////////////////
// PassThrough filter //
////////////////////////
pcl::PassThrough<pcl::PointXYZ> pass;
pass.setInputCloud (pcl_cloud);
pass.setFilterFieldName ("x");
pass.setFilterLimits (it->pose.position.x-localconfig.detection_distance-it->pose.orientation.x*0.002,
it->pose.position.x+localconfig.detection_distance+it->pose.orientation.x*0.002);
//pass.setFilterLimitsNegative (true);
pass.filter (*final2);
pass.setInputCloud (final2);
pass.setFilterFieldName ("y");
pass.setFilterLimits (it->pose.position.y-localconfig.detection_distance-it->pose.orientation.x*0.002,
it->pose.position.y+localconfig.detection_distance+it->pose.orientation.x*0.002);
//pass.setFilterLimitsNegative (true);
pass.filter (*final2);
}
else
{ // Robot seen
////////////////////////
// PassThrough filter //
////////////////////////
pcl::PassThrough<pcl::PointXYZ> pass;
pass.setInputCloud (pcl_cloud);
pass.setFilterFieldName ("x");
pass.setFilterLimits (it->pose.position.x-localconfig.detection_distance, it->pose.position.x+localconfig.detection_distance);
//pass.setFilterLimitsNegative (true);
pass.filter (*final2);
pass.setInputCloud (final2);
pass.setFilterFieldName ("y");
pass.setFilterLimits (it->pose.position.y-localconfig.detection_distance, it->pose.position.y+localconfig.detection_distance);
//pass.setFilterLimitsNegative (true);
pass.filter (*final2);
pcl::ConditionOr<pcl::PointXYZ>::Ptr range_cond (new pcl::ConditionOr<pcl::PointXYZ> ());
range_cond->addComparison (pcl::FieldComparison<pcl::PointXYZ>::ConstPtr (new
pcl::FieldComparison<pcl::PointXYZ> ("x", pcl::ComparisonOps::GT, it->pose.position.x+localconfig.detection_distance)));
range_cond->addComparison (pcl::FieldComparison<pcl::PointXYZ>::ConstPtr (new
pcl::FieldComparison<pcl::PointXYZ> ("x", pcl::ComparisonOps::LT, it->pose.position.x-localconfig.detection_distance)));
range_cond->addComparison (pcl::FieldComparison<pcl::PointXYZ>::ConstPtr (new
pcl::FieldComparison<pcl::PointXYZ> ("y", pcl::ComparisonOps::GT, it->pose.position.y+localconfig.detection_distance)));
range_cond->addComparison (pcl::FieldComparison<pcl::PointXYZ>::ConstPtr (new
pcl::FieldComparison<pcl::PointXYZ> ("y", pcl::ComparisonOps::LT, it->pose.position.y-localconfig.detection_distance)));
// build the filter
pcl::ConditionalRemoval<pcl::PointXYZ> condrem (range_cond);
condrem.setInputCloud (pcl_cloud);
condrem.setKeepOrganized(true);
// apply filter
condrem.filter (*pcl_cloud);
}
if(final2->size() > 2)
{ // Something in the box
double meanX = 0.0;
double meanY = 0.0;
for(int i = 0; i < final2->size(); i++)
{
meanX += final2->at(i).x;
meanY += final2->at(i).y;
}
meanX = meanX / final2->size();
meanY = meanY / final2->size();
//robots.at(robot_counter).pose.position.x = meanX;
//robots.at(robot_counter).pose.position.y = meanY;
it->pose.position.x = (meanX + it->pose.position.x) / 2.0;
it->pose.position.y = (meanY + it->pose.position.y) / 2.0;
char numstr[50];
sprintf(numstr, "/robot_%s_link", it->header.frame_id.c_str());
t = tf::StampedTransform(tf::Transform(tf::createQuaternionFromYaw(0.0), tf::Vector3(meanX, meanY, 0.0)),
ros::Time::now(), "/world", numstr);
t.stamp_ = ros::Time::now();
broadcaster.sendTransform(t);
it->pose.orientation.x = 0.0;
tmp_list.push_front(*it);
if(it->header.frame_id == "0")
{
robot1_output_ready = true;
}
else
{
robot2_output_ready = true;
}
//std::cout << "size : " << final2->size() << std::endl;
}
else
{ // Nothing found in the box
it->pose.orientation.x += 1.0;
if(it->pose.orientation.x > 100.0)
{
it->pose.orientation.x = 100.0;
}
//std::cout << "coef : " << it->pose.orientation.x << std::endl;
geometry_msgs::PoseStamped tmp = *it;
//robots.push_back(*it);
//robots.erase(it);
//robots.push_back(tmp);
tmp_list.push_back(*it);
}
robot_counter++;
}
robots = tmp_list;
/* protected region user implementation of subscribe callback for input_cloud end */
}
int pclTest(const int argc, char** const argv) {
if(argc == 1) {
std::cout << "Please specify an .OBJ-file!" << std::endl;
return -1;
}
if(pcl::io::loadOBJFile(argv[1], *input) == -1) {
PCL_ERROR("Couldn't read file!\n");
return -1;
}
// Conditional removal: everything y < 650
pcl::ConditionAnd<pcl::PointNormal>::Ptr range_cond(new pcl::ConditionAnd<pcl::PointNormal>());
range_cond->addComparison(pcl::FieldComparison<pcl::PointNormal>::ConstPtr(
new pcl::FieldComparison<pcl::PointNormal>("y", pcl::ComparisonOps::GT, 650.0))
);
pcl::ConditionalRemoval<pcl::PointNormal> condrem;
condrem.setCondition(range_cond);
condrem.setInputCloud(input);
condrem.setKeepOrganized(true);
condrem.filter(*demeaned);
// Conditional removal: every normal -0.5 < n_x < 0.5
/*
pcl::ConditionOr<pcl::PointNormal>::Ptr range_cond2(new pcl::ConditionOr<pcl::PointNormal>());
range_cond2->addComparison(pcl::FieldComparison<pcl::PointNormal>::ConstPtr(
new pcl::FieldComparison<pcl::PointNormal>("normal_x", pcl::ComparisonOps::GT, 0.5))
);
range_cond2->addComparison(pcl::FieldComparison<pcl::PointNormal>::ConstPtr(
new pcl::FieldComparison<pcl::PointNormal>("normal_x", pcl::ComparisonOps::LT, -0.5))
);
condrem.setCondition(range_cond2);
condrem.setInputCloud(demeaned);
condrem.setKeepOrganized(true);
condrem.filter(*demeaned);
*/
// Remove statistical outliers: neighbour distance > 2.0
pcl::StatisticalOutlierRemoval<pcl::PointNormal> sor;
sor.setInputCloud(demeaned);
sor.setMeanK(50);
sor.setStddevMulThresh(2.0);
sor.filter(*demeaned);
// Center cloud
Eigen::Vector4f centroid;
pcl::compute3DCentroid(*demeaned, centroid);
pcl::demeanPointCloud(*demeaned, centroid, *demeaned);
//pcl::PointCloud<pcl::PointXYZ>
float meanXLeft = 0;
int numXLeft = 0;
float meanXRight = 0;
int numXRight = 0;
std::vector<float> pointsXLeft;
std::vector<float> pointsXRight;
for(size_t i = 0; i < demeaned->points.size(); i++) {
if(demeaned->points[i].x > 1.0) {
meanXLeft += demeaned->points[i].x;
pointsXLeft.push_back(demeaned->points[i].x);
numXLeft++;
}
if(demeaned->points[i].x < -1.0) {
meanXRight += demeaned->points[i].x;
pointsXRight.push_back(demeaned->points[i].x);
numXRight++;
}
}
meanXLeft = (meanXLeft / numXLeft);
meanXRight = (meanXRight / numXRight);
for(float& f : pointsXLeft) {
f -= meanXLeft;
}
for(float& f : pointsXRight) {
f -= meanXRight;
}
float stdDeviationXLeft = 0;
float stdDeviationXRight = 0;
float planeMeanXLeft = 0;
float planeMeanXRight = 0;
for(float& f : pointsXLeft) {
stdDeviationXLeft += pow(f,2);
planeMeanXLeft += f;
}
stdDeviationXLeft = sqrt(stdDeviationXLeft / (pointsXLeft.size() - 1));
planeMeanXLeft = planeMeanXLeft / pointsXLeft.size();
for(float& f : pointsXRight) {
stdDeviationXRight += pow(f,2);
planeMeanXRight += f;
}
//stdDeviationXRight = sqrt((1/(pointsXRight.size() - 1))*stdDeviationXRight);
stdDeviationXRight = sqrt(stdDeviationXRight / (pointsXRight.size() - 1));
planeMeanXRight = planeMeanXRight / pointsXRight.size();
std::cout << "Mean left: " << meanXLeft << std::endl;
std::cout << "Mean right: " << meanXRight << std::endl;
std::cout << "Mean distance: " << (meanXLeft - meanXRight) << std::endl;
std::cout << std::endl;
std::cout << "Std deviation left plane: " << stdDeviationXLeft << std::endl;
std::cout << "Std deviation right plane: " << stdDeviationXRight << std::endl;
std::cout << "Mean left plane: " << planeMeanXLeft << std::endl;
std::cout << "Mean right plane: " << planeMeanXLeft << std::endl;
boost::shared_ptr<pcl::visualization::PCLVisualizer> viewer(new pcl::visualization::PCLVisualizer("Simple Cloud Viewer"));
viewer->addPointCloud<pcl::PointNormal>(demeaned, "sample cloud");
viewer->addPointCloudNormals<pcl::PointNormal, pcl::PointNormal>(demeaned,demeaned,1,2.0f);
viewer->addCoordinateSystem(1.0);
viewer->initCameraParameters();
viewer->registerMouseCallback(mouseEventOccurred, (void*)&viewer);
while(!viewer->wasStopped()) {
viewer->spinOnce(100);
boost::this_thread::sleep (boost::posix_time::microseconds (100000));
}
return 0;
}
void laserPointsCallback(const sensor_msgs::PointCloud2::ConstPtr& cloud_in)
{
tf::StampedTransform laser_transform;
if(!tf_listener->waitForTransform(
"/map",
"/head_hokuyo_frame",
cloud_in->header.stamp,
ros::Duration(1.0)))
{
ROS_WARN("Transform from /map to /head_hokuyo_frame failed");
return;
}
tf_listener->lookupTransform("/map", "/head_hokuyo_frame", cloud_in->header.stamp, laser_transform);
octomap::point3d laser_origin = octomap::pointTfToOctomap(laser_transform.getOrigin());
pcl::PointCloud<pcl::PointXYZ>::Ptr pcl_raw_cloud(new pcl::PointCloud<pcl::PointXYZ>);
pcl::fromROSMsg(*cloud_in, *pcl_raw_cloud);
// filter out the points on the robot
pcl::PointCloud<pcl::PointXYZ>::Ptr pcl_robot_filtered_cloud(new pcl::PointCloud<pcl::PointXYZ>);
pcl::ConditionOr<pcl::PointXYZ>::Ptr range_cond (new pcl::ConditionOr<pcl::PointXYZ>());
range_cond->addComparison(pcl::FieldComparison<pcl::PointXYZ>::ConstPtr (
new pcl::FieldComparison<pcl::PointXYZ>("x", pcl::ComparisonOps::GE, 0.25)));
range_cond->addComparison(pcl::FieldComparison<pcl::PointXYZ>::ConstPtr (
new pcl::FieldComparison<pcl::PointXYZ>("x", pcl::ComparisonOps::LE, -0.25)));
range_cond->addComparison(pcl::FieldComparison<pcl::PointXYZ>::ConstPtr (
new pcl::FieldComparison<pcl::PointXYZ>("y", pcl::ComparisonOps::GE, 0.25)));
range_cond->addComparison(pcl::FieldComparison<pcl::PointXYZ>::ConstPtr (
new pcl::FieldComparison<pcl::PointXYZ>("y", pcl::ComparisonOps::LE, -0.25)));
range_cond->addComparison(pcl::FieldComparison<pcl::PointXYZ>::ConstPtr (
new pcl::FieldComparison<pcl::PointXYZ>("z", pcl::ComparisonOps::GE, 1.2)));
range_cond->addComparison(pcl::FieldComparison<pcl::PointXYZ>::ConstPtr (
new pcl::FieldComparison<pcl::PointXYZ>("z", pcl::ComparisonOps::LE, -.1)));
pcl::ConditionalRemoval<pcl::PointXYZ> robot_filter(range_cond);
robot_filter.setInputCloud(pcl_raw_cloud);
robot_filter.setKeepOrganized(true);
robot_filter.filter(*pcl_robot_filtered_cloud);
ROS_DEBUG("pcl_robot_filtered_cloud size: %lu", pcl_robot_filtered_cloud->points.size());
pcl::PointCloud<pcl::PointXYZ>::Ptr pcl_map_cloud(new pcl::PointCloud<pcl::PointXYZ>);
if(!tf_listener->waitForTransform(
"/map",
"/base_link",
cloud_in->header.stamp,
ros::Duration(1.0)))
{
ROS_WARN("Transform from /map to /base_link failed");
return;
}
pcl_ros::transformPointCloud("/map",
cloud_in->header.stamp,
*pcl_robot_filtered_cloud,
"/base_link",
*pcl_map_cloud,
*tf_listener);
// filter out the ground
pcl::PointCloud<pcl::PointXYZ>::Ptr pcl_ground_filtered_cloud(new pcl::PointCloud<pcl::PointXYZ>);
pcl::PassThrough<pcl::PointXYZ> ground_filter;
ground_filter.setInputCloud(pcl_map_cloud);
ground_filter.setFilterFieldName("z");
ground_filter.setFilterLimits(-1, .05);
ground_filter.setFilterLimitsNegative(true);
ground_filter.filter(*pcl_ground_filtered_cloud);
ROS_DEBUG("pcl_ground_filtered_cloud size: %lu", pcl_ground_filtered_cloud->points.size());
// filter out the max range readings
pcl::PointIndices::Ptr max_indices(new pcl::PointIndices);
int i=0;
for(pcl::PointCloud<pcl::PointXYZ>::const_iterator point = pcl_ground_filtered_cloud->points.begin();
point != pcl_ground_filtered_cloud->points.end();
point++)
{
// if this point is within .03 m of the max sensor reading then we want to remove it
double x = point->x - laser_origin.x();
double y = point->y - laser_origin.y();
double z = point->z - laser_origin.z();
if(fabs(sqrt(x*x + y*y + z*z) - 30.0) < .04)
max_indices->indices.push_back(i);
i++;
}
pcl::PointCloud<pcl::PointXYZ>::Ptr pcl_max_filtered_cloud(new pcl::PointCloud<pcl::PointXYZ>);
pcl::ExtractIndices<pcl::PointXYZ> max_filter;
max_filter.setInputCloud(pcl_ground_filtered_cloud);
max_filter.setKeepOrganized(true);
max_filter.setNegative(true);
max_filter.setIndices(max_indices);
max_filter.filter(*pcl_max_filtered_cloud);
ROS_DEBUG("pcl_max_filtered_cloud size: %lu", pcl_max_filtered_cloud->points.size());
// convert to ros datatype as an intermediate step to converting to octomap type
sensor_msgs::PointCloud2 filtered_cloud;
pcl::toROSMsg(*pcl_max_filtered_cloud, filtered_cloud);
ROS_DEBUG("filtered_cloud size: %lu", filtered_cloud.data.size());
// convert to octomap type
octomap::Pointcloud octomap_cloud;
octomap::pointCloud2ToOctomap(filtered_cloud, octomap_cloud);
ROS_DEBUG("octomap pointcloud size: %lu", octomap_cloud.size());
tree->insertPointCloudRays(octomap_cloud, laser_origin, -1, true);
ROS_DEBUG("map_changed is now true");
map_changed = true;
}
bool filterIt(int argc, char **argv,pcl::PointCloud<PointType>::Ptr cloud, pcl::PointCloud<PointType>::Ptr cloud_filtered) {
pcl::ScopeTime time("performance");
time.reset();
if (strcmp(argv[2], "-r") == 0) {
if (argc != 5) {
std::cout << "All the parameters are not correctly set for the radius removal filter" << std::endl;
return 0;
}
pcl::RadiusOutlierRemoval<PointType> outrem;
// build the filter
outrem.setInputCloud(cloud);
outrem.setRadiusSearch(atof(argv[3]));
outrem.setMinNeighborsInRadius (atof(argv[4]));
// apply filter
outrem.filter (*cloud_filtered);
}
else if (strcmp(argv[2], "-c") == 0) {
if (argc != 6) {
std::cout << "All the parameters are not correctly set for the conditional filter" << std::endl;
return 0;
}
// build the condition
pcl::ConditionAnd<PointType>::Ptr range_cond (new pcl::ConditionAnd<PointType> ());
range_cond->addComparison (pcl::FieldComparison<PointType>::ConstPtr (new
pcl::FieldComparison<PointType> (argv[5], pcl::ComparisonOps::GT, atof ( argv[3] ))));
range_cond->addComparison (pcl::FieldComparison<PointType>::ConstPtr (new
pcl::FieldComparison<PointType> (argv[5], pcl::ComparisonOps::LT, atof(argv[4]) )));
// build the filter
pcl::ConditionalRemoval<PointType> condrem (range_cond);
condrem.setInputCloud (cloud);
condrem.setKeepOrganized(false);
// apply filter
condrem.filter (*cloud_filtered);
}
else if (strcmp(argv[2], "-s") == 0) {
if (argc != 5) {
std::cout << "All the parameters are not correctly set for the statistical filter" << std::endl;
return 0;
}
// Create the filtering object
pcl::StatisticalOutlierRemoval<PointType> sor;
sor.setInputCloud (cloud);
sor.setMeanK (atof(argv[3]));
sor.setStddevMulThresh (atof(argv[4]));
sor.filter (*cloud_filtered);
}
else if (strcmp(argv[2], "-v") == 0) {
// Create the filtering object
pcl::VoxelGrid<PointType> sor;
sor.setDownsampleAllData(true);
sor.setInputCloud (cloud);
sor.setLeafSize (atof(argv[3]), atof(argv[3]), atof(argv[3]));
sor.filter (*cloud_filtered);
}
else if (strcmp(argv[2], "-av") == 0) {
// Create the filtering object
pcl::ApproximateVoxelGrid<PointType> sor;
sor.setDownsampleAllData(true);
sor.setInputCloud (cloud);
sor.setLeafSize (atof(argv[3]), atof(argv[3]), atof(argv[3]));
sor.filter (*cloud_filtered);
}
else if (strcmp(argv[2], "-rg") == 0) {
// Create the filtering object
std::vector<pcl::IndicesPtr> clusteredIndices;
pcl::PointCloud<pcl::Normal>::Ptr cloud_normals (new pcl::PointCloud<pcl::Normal>);
pcl::RegionGrowing<PointType> sor;
//sor.setDownsampleAllData(true);
sor.setInputCloud (cloud);
sor.setGrowingDistance(atof(argv[3]));
//sor.setLeafSize (atof(argv[3]), atof(argv[3]), atof(argv[3]));
if (atof(argv[4]) > 0){
sor.setEpsAngle(atof(argv[4]));
pcl::NormalEstimation<PointType, pcl::Normal> ne;
ne.setInputCloud (cloud);
pcl::search::KdTree<PointType>::Ptr tree (new pcl::search::KdTree<PointType> ());
ne.setSearchMethod (tree);
ne.setRadiusSearch (atof(argv[3]));
ne.compute (*cloud_normals);
sor.setNormals(cloud_normals);
cout << "normals estimed" << endl;
}
sor.cluster (&clusteredIndices);
cout << "clusters? " <<clusteredIndices.size()<<endl;
classification<PointType> classif(cloud, clusteredIndices);
// pcl::search::KdTree<PointType>::Ptr tree (new pcl::search::KdTree<PointType>);
// tree->setInputCloud (cloud);
//extractEuclideanClusters (
// const PointCloud<PointT> &cloud, const boost::shared_ptr<search::Search<PointT> > &tree,
// float tolerance, std::vector<PointIndices> &clusters,
// unsigned int min_pts_per_cluster = 1, unsigned int max_pts_per_cluster = (std::numeric_limits<int>::max) ());
// boost::shared_ptr<pcl::search::KdTree<PointType> > tree (new pcl::search::KdTree<PointType>);
// tree->setInputCloud (cloud);
// std::vector<pcl::PointIndices> clusters;
// pcl::extractEuclideanClusters<PointType>(cloud, tree, 10, clusters, 100, 200);
// pcl::EuclideanClusterExtraction<PointType> ec;
// ec.setClusterTolerance ( atof(argv[3]) ); // 2cm
// ec.setSearchMethod (tree);
// ec.setInputCloud (cloud);
// ec.extract (clusteredIndices);
// cout << "clusters? " <<clusteredIndices.size()<<endl;
pcl::PointCloud<PointType>::Ptr buff_cloud (new pcl::PointCloud<PointType>);
for (int i=0; i<clusteredIndices.size(); i++) {
//cout << "ok 2" << endl;
if (clusteredIndices.operator[](i)->size() < 2300 || clusteredIndices.operator[](i)->size() > 2500) {
// cout << "ok 3 " << i << endl;
//cout << clusteredIndices[i]->size() << endl;
pcl::copyPointCloud(*cloud, clusteredIndices[i].operator*(), *buff_cloud);
cloud_filtered->operator+=(*buff_cloud);
buff_cloud->clear();
}
}
if(system("mkdir clusters"))
cout << "creating directory \"clusters\""<< endl;
saveclusters(clusteredIndices, cloud, "clusters/");
std::stringstream command;
command << "pcd_viewer ";
for (int i =0 ; i< clusteredIndices.size(); i++)
// if (clusteredIndices[i].indices.size() > 2300 && clusteredIndices[i].indices.size() < 2500)
command << "clusters/" << i << ".pcd ";
if(system (command.str().data()))
cout << "visualizing clusters" << endl;
FILE *fp;
fp = fopen ( "command.sh", "wt" ) ;
fprintf ( fp, " %s\n", command.str().data() );
fclose(fp);
}
else {
std::cerr << "please specify the point cloud and the command line arg '-r' or '-c' or '-s' or '-v' or '-av' or 'rg' + param" << std::endl;
exit(0);
}
time.getTime();
return 1;
}
void
pcl::kinfuLS::WorldModel<PointT>::getWorldAsCubes (const double size, std::vector<typename WorldModel<PointT>::PointCloudPtr> &cubes, std::vector<Eigen::Vector3f, Eigen::aligned_allocator<Eigen::Vector3f> > &transforms, double overlap)
{
if(world_->points.size () == 0)
{
PCL_INFO("The world is empty, returning nothing\n");
return;
}
PCL_INFO ("Getting world as cubes. World contains %d points.\n", world_->points.size ());
// remove nans from world cloud
world_->is_dense = false;
std::vector<int> indices;
pcl::removeNaNFromPointCloud ( *world_, *world_, indices);
PCL_INFO ("World contains %d points after nan removal.\n", world_->points.size ());
// check cube size value
double cubeSide = size;
if (cubeSide <= 0.0f)
{
PCL_ERROR ("Size of the cube must be positive and non null (%f given). Setting it to 3.0 meters.\n", cubeSide);
cubeSide = 512.0f;
}
std::cout << "cube size is set to " << cubeSide << std::endl;
// check overlap value
double step_increment = 1.0f - overlap;
if (overlap < 0.0)
{
PCL_ERROR ("Overlap ratio must be positive or null (%f given). Setting it to 0.0 procent.\n", overlap);
step_increment = 1.0f;
}
if (overlap > 1.0)
{
PCL_ERROR ("Overlap ratio must be less or equal to 1.0 (%f given). Setting it to 10 procent.\n", overlap);
step_increment = 0.1f;
}
// get world's bounding values on XYZ
PointT min, max;
pcl::getMinMax3D(*world_, min, max);
PCL_INFO ("Bounding box for the world: \n\t [%f - %f] \n\t [%f - %f] \n\t [%f - %f] \n", min.x, max.x, min.y, max.y, min.z, max.z);
PointT origin = min;
// clear returned vectors
cubes.clear();
transforms.clear();
// iterate with box filter
while (origin.x < max.x)
{
origin.y = min.y;
while (origin.y < max.y)
{
origin.z = min.z;
while (origin.z < max.z)
{
// extract cube here
PCL_INFO ("Extracting cube at: [%f, %f, %f].\n", origin.x, origin.y, origin.z);
// pointcloud for current cube.
PointCloudPtr box (new pcl::PointCloud<PointT>);
// set conditional filter
ConditionAndPtr range_cond (new pcl::ConditionAnd<PointT> ());
range_cond->addComparison (FieldComparisonConstPtr (new pcl::FieldComparison<PointT> ("x", pcl::ComparisonOps::GE, origin.x)));
range_cond->addComparison (FieldComparisonConstPtr (new pcl::FieldComparison<PointT> ("x", pcl::ComparisonOps::LT, origin.x + cubeSide)));
range_cond->addComparison (FieldComparisonConstPtr (new pcl::FieldComparison<PointT> ("y", pcl::ComparisonOps::GE, origin.y)));
range_cond->addComparison (FieldComparisonConstPtr (new pcl::FieldComparison<PointT> ("y", pcl::ComparisonOps::LT, origin.y + cubeSide)));
range_cond->addComparison (FieldComparisonConstPtr (new pcl::FieldComparison<PointT> ("z", pcl::ComparisonOps::GE, origin.z)));
range_cond->addComparison (FieldComparisonConstPtr (new pcl::FieldComparison<PointT> ("z", pcl::ComparisonOps::LT, origin.z + cubeSide)));
// build the filter
pcl::ConditionalRemoval<PointT> condrem;
condrem.setCondition (range_cond);
condrem.setInputCloud (world_);
condrem.setKeepOrganized(false);
// apply filter
condrem.filter (*box);
// also push transform along with points.
if(box->points.size() > 0)
{
Eigen::Vector3f transform;
transform[0] = origin.x, transform[1] = origin.y, transform[2] = origin.z;
transforms.push_back(transform);
cubes.push_back(box);
}
else
{
PCL_INFO ("Extracted cube was empty, skiping this one.\n");
}
origin.z += cubeSide * step_increment;
}
origin.y += cubeSide * step_increment;
}
origin.x += cubeSide * step_increment;
}
/* for(int c = 0 ; c < cubes.size() ; ++c)
{
std::stringstream name;
name << "cloud" << c+1 << ".pcd";
pcl::io::savePCDFileASCII(name.str(), *(cubes[c]));
}*/
std::cout << "returning " << cubes.size() << " cubes" << std::endl;
}
inline void
normal_diff_filter(
const pcl::PointCloud<PointT> &in,
pcl::PointCloud<PointT> &out,
double scale1 = 0.40, ///The smallest scale to use in the DoN filter.
double scale2 = 0.60, ///The largest scale to use in the DoN filter.
double threshold = 0.6 ///The minimum DoN magnitude to threshold by
)
{
boost::shared_ptr<pcl::PointCloud<PointT> > in_ptr(in.makeShared());
boost::shared_ptr<pcl::search::KdTree<PointT> > tree_n;
tree_n.reset( new pcl::search::KdTree<PointT>(false) );
tree_n->setInputCloud(in_ptr);
tree_n->setEpsilon(0.5);
if (scale1 >= scale2)
{
printf("Error: Large scale must be > small scale!");
return;
}
// Compute normals using both small and large scales at each point
pcl::NormalEstimationOMP<PointT, pcl::PointNormal> ne;
ne.setInputCloud (in_ptr);
ne.setSearchMethod (tree_n);
/**
* NOTE: setting viewpoint is very important, so that we can ensure
* normals are all pointed in the same direction!
*/
ne.setViewPoint (std::numeric_limits<float>::max (), std::numeric_limits<float>::max (), std::numeric_limits<float>::max ());
// calculate normals with the small scale
pcl::PointCloud<pcl::PointNormal>::Ptr normals_small_scale (new pcl::PointCloud<pcl::PointNormal>);
ne.setRadiusSearch (scale1);
ne.compute (*normals_small_scale);
// calculate normals with the large scale
pcl::PointCloud<pcl::PointNormal>::Ptr normals_large_scale (new pcl::PointCloud<pcl::PointNormal>);
ne.setRadiusSearch (scale2);
ne.compute (*normals_large_scale);
// Create output cloud for DoN results
pcl::PointCloud<pcl::PointNormal>::Ptr doncloud (new pcl::PointCloud<pcl::PointNormal>);
pcl::copyPointCloud<PointT, pcl::PointNormal>(in, *doncloud);
// Create DoN operator
pcl::DifferenceOfNormalsEstimation<PointT, pcl::PointNormal, pcl::PointNormal> don;
don.setInputCloud(in_ptr);
don.setNormalScaleLarge(normals_large_scale);
don.setNormalScaleSmall(normals_small_scale);
if (!don.initCompute ())
{
std::cerr << "Error: Could not intialize DoN feature operator" << std::endl;
return;
}
// Compute DoN
don.computeFeature (*doncloud);
// Build the condition for filtering
pcl::ConditionOr<pcl::PointNormal>::Ptr range_cond (
new pcl::ConditionOr<pcl::PointNormal> ()
);
range_cond->addComparison (pcl::FieldComparison<pcl::PointNormal>::ConstPtr (
new pcl::FieldComparison<pcl::PointNormal> ("curvature", pcl::ComparisonOps::GT, threshold))
);
// Build the filter
pcl::ConditionalRemoval<pcl::PointNormal> condrem (range_cond, true);
condrem.setInputCloud(doncloud);
pcl::PointCloud<pcl::PointNormal>::Ptr doncloud_filtered (new pcl::PointCloud<pcl::PointNormal>);
// Apply filter
condrem.filter(*doncloud_filtered);
boost::shared_ptr<pcl::PointIndices> indices(new pcl::PointIndices);
condrem.getRemovedIndices(*indices);
//std::cout << "Indices: " << indices->indices.size() << " Filtered: " << doncloud_filtered->size () << " data points." << std::endl;
pcl::ExtractIndices<PointT> extract;
extract.setInputCloud(in_ptr);
extract.setIndices(indices);
extract.setNegative (true);
extract.filter(out);
// Save filtered output
//std::cout << "In: " << in.size() << " Filtered: " << out.size () << " data points." << std::endl;
}
int
main (int argc, char** argv)
{
if (argc != 2)
{
std::cerr << "please specify command line arg '-r' or '-c'" << std::endl;
exit(0);
}
pcl::PointCloud<pcl::PointXYZ>::Ptr cloud (new pcl::PointCloud<pcl::PointXYZ>);
pcl::PointCloud<pcl::PointXYZ>::Ptr cloud_filtered (new pcl::PointCloud<pcl::PointXYZ>);
// Fill in the cloud data
cloud->width = 5;
cloud->height = 1;
cloud->points.resize (cloud->width * cloud->height);
for (size_t i = 0; i < cloud->points.size (); ++i)
{
cloud->points[i].x = 1024 * rand () / (RAND_MAX + 1.0);
cloud->points[i].y = 1024 * rand () / (RAND_MAX + 1.0);
cloud->points[i].z = 1024 * rand () / (RAND_MAX + 1.0);
}
if (strcmp(argv[1], "-r") == 0){
pcl::RadiusOutlierRemoval<pcl::PointXYZ> outrem;
// build the filter
outrem.setInputCloud(cloud);
outrem.setRadiusSearch(0.8);
outrem.setMinNeighborsInRadius (2);
// apply filter
outrem.filter (*cloud_filtered);
}
else if (strcmp(argv[1], "-c") == 0){
// build the condition
pcl::ConditionAnd<pcl::PointXYZ>::Ptr range_cond (new
pcl::ConditionAnd<pcl::PointXYZ> ());
range_cond->addComparison (pcl::FieldComparison<pcl::PointXYZ>::ConstPtr (new
pcl::FieldComparison<pcl::PointXYZ> ("z", pcl::ComparisonOps::GT, 0.0)));
range_cond->addComparison (pcl::FieldComparison<pcl::PointXYZ>::ConstPtr (new
pcl::FieldComparison<pcl::PointXYZ> ("z", pcl::ComparisonOps::LT, 0.8)));
// build the filter
pcl::ConditionalRemoval<pcl::PointXYZ> condrem (range_cond);
condrem.setInputCloud (cloud);
condrem.setKeepOrganized(true);
// apply filter
condrem.filter (*cloud_filtered);
}
else{
std::cerr << "please specify command line arg '-r' or '-c'" << std::endl;
exit(0);
}
std::cerr << "Cloud before filtering: " << std::endl;
for (size_t i = 0; i < cloud->points.size (); ++i)
std::cerr << " " << cloud->points[i].x << " "
<< cloud->points[i].y << " "
<< cloud->points[i].z << std::endl;
// display pointcloud after filtering
std::cerr << "Cloud after filtering: " << std::endl;
for (size_t i = 0; i < cloud_filtered->points.size (); ++i)
std::cerr << " " << cloud_filtered->points[i].x << " "
<< cloud_filtered->points[i].y << " "
<< cloud_filtered->points[i].z << std::endl;
return (0);
}
